C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Now let’s review the steps:

1.

Split your data. First, divide you data set into three pieces, a training set, a test set and a blind

test set. Use about 80% of your data records for your training set, 10% for your test set and 10% for

your blind test set.

2.

Train and test. Next, start with a network topology and train your network on your training set

data. When you have reached a satisfactory minimum error, save your weights and apply your trained

network to the test data and note the error. Now restart the process with the same network topology

for a different set of initial weights and see if you can achieve a better error on training and test sets.

Reasoning: you may have found a local minimum on your first attempt and randomizing the initial

weights will start you off to a different, maybe better solution.